Abstract
Finding Semantic similarity is an important component in various fields such as information retrieval, question-answering system, machine translation and text summarization. This paper describes two different approaches to find semantic similarity on SemEval 2016 dataset. First method is based on lexical analysis whereas second method is based on distributed semantic approach. Both approaches are trained using feed-forward neural network and layer-recurrent network to predict the similarity score.
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References
Clarke, F., Ekeland, I.: Nonlinear oscillations and boundary-value problems for Hamiltonian systems. Arch. Rat. Mech. Anal. 78, 315–333 (1982)
Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of LREC 2010 Workshop New Challenges for NLP Frameworks, p. 4550 (2010)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Agirre, E., Baneab, C., Cardiec, C., Cerd, D., Diabe, M., Gonzalez-Agirrea, A., Guof, W., Lopez-Gazpioa, I., Maritxalara, M., Mihalcea, R., Rigau, G., Uria, L., Wiebe, J.: SemEval- 2015 task 2: semantic textual similarity, English, Spanish and Pilot on interpretability. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 252–263 (2015)
Agirre, E., Baneab, C., Cer, D., Diab, M., Gonzalez-Agirree, A., Mihalceab, R., Wiebe, J.: SemEval-2016 task 1: Semantic textual similarity - monolingual and cross-lingual evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016) (2016)
Denkowski, M., Lavie, A.: Extending the METEOR machine translation evaluation metric to the phrase level. In: Proceedings of the HLT: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, pp. 250–253 (2010)
Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL 2005 Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization, Ann Arbor, Michigan, pp. 65–72 (2005)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Dokl. 10, 707 (1996)
Riedmiller, M., Braun, H.: RPROP: a fast adaptive learning algorithm. In: Gelenbe, E. (ed.) International Symposium on Computer and Information Science VII, Antalya, Turkey, pp. 279–286 (1992)
Huang, A.: Similarity measures for text document clustering. In: Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC 2008), Christchurch, New Zealand, pp. 49–56 (2010)
Aziz, M., Rafi, M.: Sentence based semantic similarity measure for blog-posts digital content. In: 2010 6th International Conference on Multimedia Technology and Its Applications (IDC), pp. 69–74 (2010)
Achananuparp, P., Hu, X., Shen, X.: The evaluation of sentence similarity measures data warehousing and knowledge discovery. In: Proceedings of the 10th International Conference, DaWaK 2008, Turin, Italy, 2–5 September 2008, pp. 305–316 (2008)
Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013)
Acknowledgment
This work presented here is under the Research Project Grant No. YSS/2015/000988 under Science and Engineering Research Board (SERB), Govt. of India. Authors are also acknowledges the Department of Computer Science & Engineering of National Institute of Technology Mizoram, India for providing infrastructural facilities and support.
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Sarkar, S., Pakray, P., Das, D., Gelbukh, A. (2017). Regression Based Approaches for Detecting and Measuring Textual Similarity. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_14
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